Social Welfare Analysis of China’s High-Speed Rail Industry: Based on the Perspective of Enterprises’ Entry in Upstream Market
Bibliographic record
Abstract
Based on the analysis of the high-speed rail industry chain, first, this paper divides the high-speed rail industry chain into infrastructure construction market and manufacturing market of mobile equipment and, second, this paper uses the empirical method of new experience industry organization to measure the market power premium of the high-speed rail upstream market. The study shows that the market power premium of the high-speed rail upstream market is 0.551, and the scale elasticity is 0.314, indicating that there is no systematic market power in the high-speed rail upstream market and there is significant scale diseconomy. The vertical market structure where “private enterprises dominate the upstream competition market and state-owned enterprises dominate the downstream oligopoly market” is further established. Based on the perspective of enterprises’ entry in upstream markets, the social welfare of the high-speed rail industry market structure is analyzed. It is found in the study that the upstream market of the high-speed rail industry has a tendency of insufficient enterprise entry, and the total social welfare increases with the increase in the number of upstream enterprises entry. What is more, the profit of enterprises in the upstream market of high-speed rail decreases with the increase in the number of enterprises in the upstream. This paper believes that policies such as stimulating upstream high-speed rail enterprises entry, providing subsidies to upstream enterprises, reducing upstream enterprises’ entry barriers, and expanding international markets can effectively improve the overall social welfare of the high-speed railway industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".